Overview

Dataset statistics

Number of variables28
Number of observations108
Missing cells63
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.8 KiB
Average record size in memory225.2 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-16" Constant
url has a high cardinality: 108 distinct values High cardinality
name has a high cardinality: 100 distinct values High cardinality
_embedded_show_url has a high cardinality: 69 distinct values High cardinality
_embedded_show_name has a high cardinality: 69 distinct values High cardinality
_embedded_show_premiered has a high cardinality: 56 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 62 distinct values High cardinality
_embedded_show_summary has a high cardinality: 59 distinct values High cardinality
_links_self_href has a high cardinality: 108 distinct values High cardinality
season is highly correlated with _embedded_show_updatedHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_updated is highly correlated with seasonHigh correlation
season is highly correlated with number and 1 other fieldsHigh correlation
number is highly correlated with season and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with season and 3 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
name is highly correlated with summary and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_ended and 13 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
summary is highly correlated with name and 2 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
airdate is highly correlated with name and 15 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
id is highly correlated with name and 10 other fieldsHigh correlation
name is highly correlated with id and 21 other fieldsHigh correlation
season is highly correlated with name and 11 other fieldsHigh correlation
number is highly correlated with name and 13 other fieldsHigh correlation
type is highly correlated with name and 10 other fieldsHigh correlation
airtime is highly correlated with name and 16 other fieldsHigh correlation
airstamp is highly correlated with name and 19 other fieldsHigh correlation
runtime is highly correlated with name and 17 other fieldsHigh correlation
summary is highly correlated with name and 3 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_type is highly correlated with name and 18 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_status is highly correlated with name and 13 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with name and 17 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with name and 19 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_ended is highly correlated with id and 11 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_weight is highly correlated with name and 14 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with name and 12 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 14 other fieldsHigh correlation
number has 2 (1.9%) missing values Missing
runtime has 10 (9.3%) missing values Missing
_embedded_show_runtime has 45 (41.7%) missing values Missing
_embedded_show_averageRuntime has 6 (5.6%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:14:08.332159
Analysis finished2022-05-10 02:14:58.478747
Duration50.15 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2027675.056
Minimum1945146
Maximum2318106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:14:58.584183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1945146
5-th percentile1960836.8
Q11984558.5
median1987994.5
Q32047460
95-th percentile2188934.65
Maximum2318106
Range372960
Interquartile range (IQR)62901.5

Descriptive statistics

Standard deviation77713.36423
Coefficient of variation (CV)0.03832634031
Kurtosis1.305068419
Mean2027675.056
Median Absolute Deviation (MAD)10350
Skewness1.495939632
Sum218988906
Variance6039366979
MonotonicityNot monotonic
2022-05-09T21:14:58.767261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21796131
 
0.9%
19849521
 
0.9%
19879981
 
0.9%
19879971
 
0.9%
19879961
 
0.9%
19879951
 
0.9%
19879941
 
0.9%
19879921
 
0.9%
19879911
 
0.9%
19870331
 
0.9%
Other values (98)98
90.7%
ValueCountFrequency (%)
19451461
0.9%
19458681
0.9%
19459001
0.9%
19459011
0.9%
19585741
0.9%
19588671
0.9%
19644951
0.9%
19702061
0.9%
19720271
0.9%
19760401
0.9%
ValueCountFrequency (%)
23181061
0.9%
22059751
0.9%
22059741
0.9%
21975961
0.9%
21972841
0.9%
21926241
0.9%
21820831
0.9%
21796131
0.9%
21761331
0.9%
21749001
0.9%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size992.0 B
https://www.tvmaze.com/episodes/2179613/kontakty-1x30-kontakty-v-telefone-ili-makarova-ruslan-belyj-guram-amaran-andrej-beburisvili-sasa-vas
 
1
https://www.tvmaze.com/episodes/1984952/dream-detective-1x13-episode-13
 
1
https://www.tvmaze.com/episodes/1987998/rompan-todo-la-historia-del-rock-en-america-latina-1x05-un-solo-continente
 
1
https://www.tvmaze.com/episodes/1987997/rompan-todo-la-historia-del-rock-en-america-latina-1x04-rock-en-tu-idioma
 
1
https://www.tvmaze.com/episodes/1987996/rompan-todo-la-historia-del-rock-en-america-latina-1x03-musica-a-colores
 
1
Other values (103)
103 

Length

Max length161
Median length112
Mean length82.49074074
Min length62

Characters and Unicode

Total characters8909
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2179613/kontakty-1x30-kontakty-v-telefone-ili-makarova-ruslan-belyj-guram-amaran-andrej-beburisvili-sasa-vas
2nd rowhttps://www.tvmaze.com/episodes/1983259/mertvye-dusi-1x03-seria-3
3rd rowhttps://www.tvmaze.com/episodes/1983260/mertvye-dusi-1x04-seria-4
4th rowhttps://www.tvmaze.com/episodes/1997413/wan-sheng-jie-2x12-ah-goodbye-friends
5th rowhttps://www.tvmaze.com/episodes/2095628/yi-nian-yong-heng-1x21-episode-21

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179613/kontakty-1x30-kontakty-v-telefone-ili-makarova-ruslan-belyj-guram-amaran-andrej-beburisvili-sasa-vas1
 
0.9%
https://www.tvmaze.com/episodes/1984952/dream-detective-1x13-episode-131
 
0.9%
https://www.tvmaze.com/episodes/1987998/rompan-todo-la-historia-del-rock-en-america-latina-1x05-un-solo-continente1
 
0.9%
https://www.tvmaze.com/episodes/1987997/rompan-todo-la-historia-del-rock-en-america-latina-1x04-rock-en-tu-idioma1
 
0.9%
https://www.tvmaze.com/episodes/1987996/rompan-todo-la-historia-del-rock-en-america-latina-1x03-musica-a-colores1
 
0.9%
https://www.tvmaze.com/episodes/1987995/rompan-todo-la-historia-del-rock-en-america-latina-1x02-la-represion1
 
0.9%
https://www.tvmaze.com/episodes/1987994/rompan-todo-la-historia-del-rock-en-america-latina-1x01-la-rebeldia1
 
0.9%
https://www.tvmaze.com/episodes/1987992/the-expanse-aftershow-1x03-thomas-jane1
 
0.9%
https://www.tvmaze.com/episodes/1987991/the-expanse-aftershow-1x02-steven-strait-breck-eisner1
 
0.9%
https://www.tvmaze.com/episodes/1987033/the-expanse-aftershow-1x01-naren-shankar1
 
0.9%
Other values (98)98
90.7%

Length

2022-05-09T21:14:59.021938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2179613/kontakty-1x30-kontakty-v-telefone-ili-makarova-ruslan-belyj-guram-amaran-andrej-beburisvili-sasa-vas1
 
0.9%
https://www.tvmaze.com/episodes/1977324/stjernestov-1x16-episode-161
 
0.9%
https://www.tvmaze.com/episodes/1983260/mertvye-dusi-1x04-seria-41
 
0.9%
https://www.tvmaze.com/episodes/1997413/wan-sheng-jie-2x12-ah-goodbye-friends1
 
0.9%
https://www.tvmaze.com/episodes/2095628/yi-nian-yong-heng-1x21-episode-211
 
0.9%
https://www.tvmaze.com/episodes/2096298/no-turning-back-romance-1x04-41
 
0.9%
https://www.tvmaze.com/episodes/2030020/dolls-frontline-2x12-episode-121
 
0.9%
https://www.tvmaze.com/episodes/2066369/chu-feng-yi-dian-shizi-1x06-episode-61
 
0.9%
https://www.tvmaze.com/episodes/2071481/youths-in-the-breeze-1x11-people-from-the-story-031
 
0.9%
https://www.tvmaze.com/episodes/2071482/youths-in-the-breeze-1x12-people-from-the-story-041
 
0.9%
Other values (98)98
90.7%

Most occurring characters

ValueCountFrequency (%)
e759
 
8.5%
-722
 
8.1%
t556
 
6.2%
/540
 
6.1%
s533
 
6.0%
o500
 
5.6%
a409
 
4.6%
w364
 
4.1%
i353
 
4.0%
m326
 
3.7%
Other values (30)3847
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6117
68.7%
Decimal Number1206
 
13.5%
Other Punctuation864
 
9.7%
Dash Punctuation722
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e759
12.4%
t556
 
9.1%
s533
 
8.7%
o500
 
8.2%
a409
 
6.7%
w364
 
6.0%
i353
 
5.8%
m326
 
5.3%
p322
 
5.3%
d249
 
4.1%
Other values (16)1746
28.5%
Decimal Number
ValueCountFrequency (%)
1293
24.3%
9157
13.0%
0144
11.9%
2133
11.0%
790
 
7.5%
888
 
7.3%
382
 
6.8%
681
 
6.7%
471
 
5.9%
567
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/540
62.5%
.216
 
25.0%
:108
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-722
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6117
68.7%
Common2792
31.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e759
12.4%
t556
 
9.1%
s533
 
8.7%
o500
 
8.2%
a409
 
6.7%
w364
 
6.0%
i353
 
5.8%
m326
 
5.3%
p322
 
5.3%
d249
 
4.1%
Other values (16)1746
28.5%
Common
ValueCountFrequency (%)
-722
25.9%
/540
19.3%
1293
10.5%
.216
 
7.7%
9157
 
5.6%
0144
 
5.2%
2133
 
4.8%
:108
 
3.9%
790
 
3.2%
888
 
3.2%
Other values (4)301
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII8909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e759
 
8.5%
-722
 
8.1%
t556
 
6.2%
/540
 
6.1%
s533
 
6.0%
o500
 
5.6%
a409
 
4.6%
w364
 
4.1%
i353
 
4.0%
m326
 
3.7%
Other values (30)3847
43.2%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct100
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size992.0 B
Episode 21
 
2
Episode 8
 
2
Episode 12
 
2
Episode 6
 
2
Episode 14
 
2
Other values (95)
98 

Length

Max length97
Median length64
Mean length18.44444444
Min length1

Characters and Unicode

Total characters1992
Distinct characters131
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)85.2%

Sample

1st rowКОНТАКТЫ в телефоне Ильи Макарова: Руслан Белый, Гурам Амарян, Андрей Бебуришвили, Саша Ваш
2nd rowСерия 3
3rd rowСерия 4
4th rowAh, Goodbye Friends
5th rowEpisode 21

Common Values

ValueCountFrequency (%)
Episode 212
 
1.9%
Episode 82
 
1.9%
Episode 122
 
1.9%
Episode 62
 
1.9%
Episode 142
 
1.9%
Episode 132
 
1.9%
Episode 22
 
1.9%
Episode 52
 
1.9%
КОНТАКТЫ в телефоне Ильи Макарова: Руслан Белый, Гурам Амарян, Андрей Бебуришвили, Саша Ваш1
 
0.9%
Episode 341
 
0.9%
Other values (90)90
83.3%

Length

2022-05-09T21:14:59.329093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode36
 
10.0%
the11
 
3.1%
9
 
2.5%
a5
 
1.4%
24
 
1.1%
la3
 
0.8%
163
 
0.8%
43
 
0.8%
december2
 
0.6%
32
 
0.6%
Other values (257)281
78.3%

Most occurring characters

ValueCountFrequency (%)
251
 
12.6%
e151
 
7.6%
o106
 
5.3%
i100
 
5.0%
a86
 
4.3%
s82
 
4.1%
n69
 
3.5%
d66
 
3.3%
r64
 
3.2%
t57
 
2.9%
Other values (121)960
48.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1269
63.7%
Uppercase Letter337
 
16.9%
Space Separator251
 
12.6%
Decimal Number91
 
4.6%
Other Punctuation36
 
1.8%
Dash Punctuation4
 
0.2%
Math Symbol2
 
0.1%
Open Punctuation1
 
0.1%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e151
 
11.9%
o106
 
8.4%
i100
 
7.9%
a86
 
6.8%
s82
 
6.5%
n69
 
5.4%
d66
 
5.2%
r64
 
5.0%
t57
 
4.5%
p44
 
3.5%
Other values (51)444
35.0%
Uppercase Letter
ValueCountFrequency (%)
E50
 
14.8%
T19
 
5.6%
S16
 
4.7%
M14
 
4.2%
D14
 
4.2%
R13
 
3.9%
L12
 
3.6%
H11
 
3.3%
O11
 
3.3%
N10
 
3.0%
Other values (37)167
49.6%
Decimal Number
ValueCountFrequency (%)
122
24.2%
215
16.5%
313
14.3%
49
9.9%
09
9.9%
67
 
7.7%
55
 
5.5%
85
 
5.5%
73
 
3.3%
93
 
3.3%
Other Punctuation
ValueCountFrequency (%)
,17
47.2%
'4
 
11.1%
&3
 
8.3%
.3
 
8.3%
:3
 
8.3%
?3
 
8.3%
#2
 
5.6%
/1
 
2.8%
Space Separator
ValueCountFrequency (%)
251
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1308
65.7%
Common386
 
19.4%
Cyrillic298
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e151
 
11.5%
o106
 
8.1%
i100
 
7.6%
a86
 
6.6%
s82
 
6.3%
n69
 
5.3%
d66
 
5.0%
r64
 
4.9%
t57
 
4.4%
E50
 
3.8%
Other values (45)477
36.5%
Cyrillic
ValueCountFrequency (%)
а32
 
10.7%
е18
 
6.0%
р17
 
5.7%
н16
 
5.4%
и14
 
4.7%
о11
 
3.7%
т10
 
3.4%
О9
 
3.0%
в9
 
3.0%
К8
 
2.7%
Other values (43)154
51.7%
Common
ValueCountFrequency (%)
251
65.0%
122
 
5.7%
,17
 
4.4%
215
 
3.9%
313
 
3.4%
49
 
2.3%
09
 
2.3%
67
 
1.8%
55
 
1.3%
85
 
1.3%
Other values (13)33
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1684
84.5%
Cyrillic298
 
15.0%
None10
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
251
 
14.9%
e151
 
9.0%
o106
 
6.3%
i100
 
5.9%
a86
 
5.1%
s82
 
4.9%
n69
 
4.1%
d66
 
3.9%
r64
 
3.8%
t57
 
3.4%
Other values (61)652
38.7%
Cyrillic
ValueCountFrequency (%)
а32
 
10.7%
е18
 
6.0%
р17
 
5.7%
н16
 
5.4%
и14
 
4.7%
о11
 
3.7%
т10
 
3.4%
О9
 
3.0%
в9
 
3.0%
К8
 
2.7%
Other values (43)154
51.7%
None
ValueCountFrequency (%)
ó3
30.0%
í2
20.0%
å1
 
10.0%
ø1
 
10.0%
ã1
 
10.0%
ç1
 
10.0%
ú1
 
10.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.78703704
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:14:59.524976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile26.45
Maximum2020
Range2019
Interquartile range (IQR)1

Descriptive statistics

Standard deviation425.9482012
Coefficient of variation (CV)4.446825107
Kurtosis17.50024661
Mean95.78703704
Median Absolute Deviation (MAD)0
Skewness4.378885764
Sum10345
Variance181431.8701
MonotonicityNot monotonic
2022-05-09T21:14:59.655860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
175
69.4%
27
 
6.5%
55
 
4.6%
20205
 
4.6%
45
 
4.6%
33
 
2.8%
73
 
2.8%
101
 
0.9%
81
 
0.9%
181
 
0.9%
Other values (2)2
 
1.9%
ValueCountFrequency (%)
175
69.4%
27
 
6.5%
33
 
2.8%
45
 
4.6%
55
 
4.6%
73
 
2.8%
81
 
0.9%
101
 
0.9%
141
 
0.9%
181
 
0.9%
ValueCountFrequency (%)
20205
4.6%
311
 
0.9%
181
 
0.9%
141
 
0.9%
101
 
0.9%
81
 
0.9%
73
2.8%
55
4.6%
45
4.6%
33
2.8%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct46
Distinct (%)43.4%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean27.06603774
Minimum1
Maximum343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:14:59.871560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q328.25
95-th percentile79
Maximum343
Range342
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation55.35758543
Coefficient of variation (CV)2.045278513
Kurtosis20.86067608
Mean27.06603774
Median Absolute Deviation (MAD)8
Skewness4.398475577
Sum2869
Variance3064.462264
MonotonicityNot monotonic
2022-05-09T21:15:00.110043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
310
 
9.3%
19
 
8.3%
29
 
8.3%
46
 
5.6%
66
 
5.6%
115
 
4.6%
55
 
4.6%
84
 
3.7%
124
 
3.7%
303
 
2.8%
Other values (36)45
41.7%
ValueCountFrequency (%)
19
8.3%
29
8.3%
310
9.3%
46
5.6%
55
4.6%
66
5.6%
71
 
0.9%
84
 
3.7%
92
 
1.9%
115
4.6%
ValueCountFrequency (%)
3431
0.9%
3061
0.9%
3051
0.9%
1531
0.9%
1021
0.9%
831
0.9%
671
0.9%
631
0.9%
621
0.9%
572
1.9%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
regular
106 
significant_special
 
2

Length

Max length19
Median length7
Mean length7.222222222
Min length7

Characters and Unicode

Total characters780
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular106
98.1%
significant_special2
 
1.9%

Length

2022-05-09T21:15:00.341130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:15:00.565107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular106
98.1%
significant_special2
 
1.9%

Most occurring characters

ValueCountFrequency (%)
r212
27.2%
a110
14.1%
e108
13.8%
g108
13.8%
l108
13.8%
u106
13.6%
i8
 
1.0%
s4
 
0.5%
n4
 
0.5%
c4
 
0.5%
Other values (4)8
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter778
99.7%
Connector Punctuation2
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r212
27.2%
a110
14.1%
e108
13.9%
g108
13.9%
l108
13.9%
u106
13.6%
i8
 
1.0%
s4
 
0.5%
n4
 
0.5%
c4
 
0.5%
Other values (3)6
 
0.8%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin778
99.7%
Common2
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r212
27.2%
a110
14.1%
e108
13.9%
g108
13.9%
l108
13.9%
u106
13.6%
i8
 
1.0%
s4
 
0.5%
n4
 
0.5%
c4
 
0.5%
Other values (3)6
 
0.8%
Common
ValueCountFrequency (%)
_2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r212
27.2%
a110
14.1%
e108
13.8%
g108
13.8%
l108
13.8%
u106
13.6%
i8
 
1.0%
s4
 
0.5%
n4
 
0.5%
c4
 
0.5%
Other values (4)8
 
1.0%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
2020-12-16
108 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1080
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-16
2nd row2020-12-16
3rd row2020-12-16
4th row2020-12-16
5th row2020-12-16

Common Values

ValueCountFrequency (%)
2020-12-16108
100.0%

Length

2022-05-09T21:15:00.739787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:15:00.942202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-16108
100.0%

Most occurring characters

ValueCountFrequency (%)
2324
30.0%
0216
20.0%
-216
20.0%
1216
20.0%
6108
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number864
80.0%
Dash Punctuation216
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2324
37.5%
0216
25.0%
1216
25.0%
6108
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1080
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2324
30.0%
0216
20.0%
-216
20.0%
1216
20.0%
6108
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2324
30.0%
0216
20.0%
-216
20.0%
1216
20.0%
6108
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size992.0 B
nan
79 
20:00
17 
10:00
 
3
12:00
 
2
00:00
 
2
Other values (5)
 
5

Length

Max length5
Median length3
Mean length3.537037037
Min length3

Characters and Unicode

Total characters382
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)4.6%

Sample

1st row12:00
2nd rownan
3rd rownan
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
nan79
73.1%
20:0017
 
15.7%
10:003
 
2.8%
12:002
 
1.9%
00:002
 
1.9%
06:001
 
0.9%
17:351
 
0.9%
09:001
 
0.9%
08:301
 
0.9%
19:001
 
0.9%

Length

2022-05-09T21:15:01.117092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:15:01.331595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan79
73.1%
20:0017
 
15.7%
10:003
 
2.8%
12:002
 
1.9%
00:002
 
1.9%
06:001
 
0.9%
17:351
 
0.9%
09:001
 
0.9%
08:301
 
0.9%
19:001
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n158
41.4%
082
21.5%
a79
20.7%
:29
 
7.6%
219
 
5.0%
17
 
1.8%
32
 
0.5%
92
 
0.5%
61
 
0.3%
71
 
0.3%
Other values (2)2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter237
62.0%
Decimal Number116
30.4%
Other Punctuation29
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
082
70.7%
219
 
16.4%
17
 
6.0%
32
 
1.7%
92
 
1.7%
61
 
0.9%
71
 
0.9%
51
 
0.9%
81
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
n158
66.7%
a79
33.3%
Other Punctuation
ValueCountFrequency (%)
:29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237
62.0%
Common145
38.0%

Most frequent character per script

Common
ValueCountFrequency (%)
082
56.6%
:29
 
20.0%
219
 
13.1%
17
 
4.8%
32
 
1.4%
92
 
1.4%
61
 
0.7%
71
 
0.7%
51
 
0.7%
81
 
0.7%
Latin
ValueCountFrequency (%)
n158
66.7%
a79
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n158
41.4%
082
21.5%
a79
20.7%
:29
 
7.6%
219
 
5.0%
17
 
1.8%
32
 
0.5%
92
 
0.5%
61
 
0.3%
71
 
0.3%
Other values (2)2
 
0.5%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size992.0 B
2020-12-16T12:00:00+00:00
74 
2020-12-16T04:00:00+00:00
 
7
2020-12-16T11:00:00+00:00
 
6
2020-12-16T00:00:00+00:00
 
3
2020-12-16T10:00:00+00:00
 
2
Other values (13)
16 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2700
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)9.3%

Sample

1st row2020-12-16T00:00:00+00:00
2nd row2020-12-16T00:00:00+00:00
3rd row2020-12-16T00:00:00+00:00
4th row2020-12-16T02:00:00+00:00
5th row2020-12-16T02:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-16T12:00:00+00:0074
68.5%
2020-12-16T04:00:00+00:007
 
6.5%
2020-12-16T11:00:00+00:006
 
5.6%
2020-12-16T00:00:00+00:003
 
2.8%
2020-12-16T10:00:00+00:002
 
1.9%
2020-12-16T02:00:00+00:002
 
1.9%
2020-12-16T15:00:00+00:002
 
1.9%
2020-12-16T17:00:00+00:002
 
1.9%
2020-12-16T05:00:00+00:001
 
0.9%
2020-12-16T05:35:00+00:001
 
0.9%
Other values (8)8
 
7.4%

Length

2022-05-09T21:15:01.522367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-16t12:00:00+00:0074
68.5%
2020-12-16t04:00:00+00:007
 
6.5%
2020-12-16t11:00:00+00:006
 
5.6%
2020-12-16t00:00:00+00:003
 
2.8%
2020-12-16t10:00:00+00:002
 
1.9%
2020-12-16t02:00:00+00:002
 
1.9%
2020-12-16t15:00:00+00:002
 
1.9%
2020-12-16t17:00:00+00:002
 
1.9%
2020-12-16t13:30:00+00:001
 
0.9%
2020-12-17t01:00:00+00:001
 
0.9%
Other values (8)8
 
7.4%

Most occurring characters

ValueCountFrequency (%)
01101
40.8%
2400
 
14.8%
:324
 
12.0%
1312
 
11.6%
-216
 
8.0%
T108
 
4.0%
+108
 
4.0%
6106
 
3.9%
48
 
0.3%
56
 
0.2%
Other values (3)11
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1944
72.0%
Other Punctuation324
 
12.0%
Dash Punctuation216
 
8.0%
Uppercase Letter108
 
4.0%
Math Symbol108
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01101
56.6%
2400
 
20.6%
1312
 
16.0%
6106
 
5.5%
48
 
0.4%
56
 
0.3%
75
 
0.3%
34
 
0.2%
92
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:324
100.0%
Dash Punctuation
ValueCountFrequency (%)
-216
100.0%
Uppercase Letter
ValueCountFrequency (%)
T108
100.0%
Math Symbol
ValueCountFrequency (%)
+108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2592
96.0%
Latin108
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01101
42.5%
2400
 
15.4%
:324
 
12.5%
1312
 
12.0%
-216
 
8.3%
+108
 
4.2%
6106
 
4.1%
48
 
0.3%
56
 
0.2%
75
 
0.2%
Other values (2)6
 
0.2%
Latin
ValueCountFrequency (%)
T108
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01101
40.8%
2400
 
14.8%
:324
 
12.0%
1312
 
11.6%
-216
 
8.0%
T108
 
4.0%
+108
 
4.0%
6106
 
3.9%
48
 
0.3%
56
 
0.2%
Other values (3)11
 
0.4%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)38.8%
Missing10
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean39.66326531
Minimum2
Maximum161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:15:01.716754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q124.25
median45
Q345
95-th percentile94.5
Maximum161
Range159
Interquartile range (IQR)20.75

Descriptive statistics

Standard deviation27.05090424
Coefficient of variation (CV)0.6820140508
Kurtosis5.368639075
Mean39.66326531
Median Absolute Deviation (MAD)13.5
Skewness1.830924718
Sum3887
Variance731.7514202
MonotonicityNot monotonic
2022-05-09T21:15:01.866285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4526
24.1%
305
 
4.6%
524
 
3.7%
1204
 
3.7%
124
 
3.7%
53
 
2.8%
73
 
2.8%
193
 
2.8%
433
 
2.8%
203
 
2.8%
Other values (28)40
37.0%
(Missing)10
 
9.3%
ValueCountFrequency (%)
21
 
0.9%
42
1.9%
53
2.8%
61
 
0.9%
73
2.8%
81
 
0.9%
111
 
0.9%
124
3.7%
131
 
0.9%
193
2.8%
ValueCountFrequency (%)
1611
 
0.9%
1204
3.7%
901
 
0.9%
791
 
0.9%
603
2.8%
551
 
0.9%
531
 
0.9%
524
3.7%
512
1.9%
501
 
0.9%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Memory size992.0 B
nan
74 
<p>When the band Peace and Love began chanting, "We got the power!" at the first rock festival in Mexico in 1971, the government responded by banning rock.</p><p><br /> </p>
 
1
<p>To get back in her family's good graces, Tumi looks for the missing best man. Against their better judgement, Tumi and Beauty seek out a family member.</p><p><br /> </p>
 
1
<p>Tumi shocks both families and devastates Beauty by letting slip a secret. But this time, after some soul searching, she decides to face the music.</p>
 
1
<p>Ty Franck and Wes Chatham welcome Executive Producer &amp; Showrunner, Naren Shankar, to chat through how they adapted Nemesis Games to Season 5, The Churn, and what the hell a showrunner does.</p>
 
1
Other values (30)
30 

Length

Max length419
Median length3
Mean length59.0462963
Min length3

Characters and Unicode

Total characters6377
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)31.5%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan74
68.5%
<p>When the band Peace and Love began chanting, "We got the power!" at the first rock festival in Mexico in 1971, the government responded by banning rock.</p><p><br /> </p>1
 
0.9%
<p>To get back in her family's good graces, Tumi looks for the missing best man. Against their better judgement, Tumi and Beauty seek out a family member.</p><p><br /> </p>1
 
0.9%
<p>Tumi shocks both families and devastates Beauty by letting slip a secret. But this time, after some soul searching, she decides to face the music.</p>1
 
0.9%
<p>Ty Franck and Wes Chatham welcome Executive Producer &amp; Showrunner, Naren Shankar, to chat through how they adapted Nemesis Games to Season 5, The Churn, and what the hell a showrunner does.</p>1
 
0.9%
<p>Steven Strait (Lead Actor &amp; Producer) and Breck Eisner (Director) join Wes Chatham and Ty Franck on this episode of the official The Expanse After show to discuss the contentious relationship between writer and director, insulting Steven Spielberg, the concept of family for Belters, capturing Baltimore in a totally uncomplicated way, and the once-a-season Simpsons easter egg.</p>1
 
0.9%
<p>Ty and Wes sit down with Thomas Jane, a man who wears many hats—that's not a Miller joke, he directed Episode 3 in Season 5! Watch them break down Thomas' new role as a director, diverting from the scripts, and the impact of seeing Alien (1979) as children.</p>1
 
0.9%
<p>Latin America's rock movement was sparked by Ritchie Valens' "La Bamba" and the Beatles but found its own voice in youth and resistance to dictatorship.</p><p><br /> </p>1
 
0.9%
<p>After the fall of the Argentine dictatorship in 1983 and the Mexico City earthquake in 1985, rock explodes with ingenuity. And it's all in Spanish.</p><p><br /> </p>1
 
0.9%
<p>More than five years after the first attack, the police finally make an arrest, setting off a media frenzy and accusations of a botched investigation.</p>1
 
0.9%
Other values (25)25
 
23.1%

Length

2022-05-09T21:15:02.197965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan74
 
6.8%
the63
 
5.8%
and46
 
4.2%
to35
 
3.2%
of22
 
2.0%
a21
 
1.9%
in20
 
1.8%
17
 
1.6%
p17
 
1.6%
her15
 
1.4%
Other values (568)754
69.6%

Most occurring characters

ValueCountFrequency (%)
959
15.0%
e541
 
8.5%
n468
 
7.3%
a455
 
7.1%
t398
 
6.2%
o347
 
5.4%
i312
 
4.9%
r293
 
4.6%
s289
 
4.5%
h204
 
3.2%
Other values (68)2111
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4685
73.5%
Space Separator976
 
15.3%
Uppercase Letter232
 
3.6%
Math Symbol226
 
3.5%
Other Punctuation204
 
3.2%
Decimal Number34
 
0.5%
Dash Punctuation14
 
0.2%
Close Punctuation3
 
< 0.1%
Open Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e541
11.5%
n468
 
10.0%
a455
 
9.7%
t398
 
8.5%
o347
 
7.4%
i312
 
6.7%
r293
 
6.3%
s289
 
6.2%
h204
 
4.4%
p187
 
4.0%
Other values (18)1191
25.4%
Uppercase Letter
ValueCountFrequency (%)
A31
13.4%
S27
11.6%
T21
 
9.1%
M17
 
7.3%
B14
 
6.0%
L13
 
5.6%
R12
 
5.2%
C12
 
5.2%
W11
 
4.7%
E9
 
3.9%
Other values (13)65
28.0%
Other Punctuation
ValueCountFrequency (%)
/65
31.9%
.51
25.0%
,49
24.0%
'22
 
10.8%
!5
 
2.5%
"4
 
2.0%
?3
 
1.5%
;2
 
1.0%
&2
 
1.0%
:1
 
0.5%
Decimal Number
ValueCountFrequency (%)
18
23.5%
97
20.6%
54
11.8%
74
11.8%
23
 
8.8%
83
 
8.8%
32
 
5.9%
02
 
5.9%
41
 
2.9%
Space Separator
ValueCountFrequency (%)
959
98.3%
 17
 
1.7%
Math Symbol
ValueCountFrequency (%)
<113
50.0%
>113
50.0%
Dash Punctuation
ValueCountFrequency (%)
-11
78.6%
3
 
21.4%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4917
77.1%
Common1460
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e541
 
11.0%
n468
 
9.5%
a455
 
9.3%
t398
 
8.1%
o347
 
7.1%
i312
 
6.3%
r293
 
6.0%
s289
 
5.9%
h204
 
4.1%
p187
 
3.8%
Other values (41)1423
28.9%
Common
ValueCountFrequency (%)
959
65.7%
<113
 
7.7%
>113
 
7.7%
/65
 
4.5%
.51
 
3.5%
,49
 
3.4%
'22
 
1.5%
 17
 
1.2%
-11
 
0.8%
18
 
0.5%
Other values (17)52
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII6355
99.7%
None19
 
0.3%
Punctuation3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
959
15.1%
e541
 
8.5%
n468
 
7.4%
a455
 
7.2%
t398
 
6.3%
o347
 
5.5%
i312
 
4.9%
r293
 
4.6%
s289
 
4.5%
h204
 
3.2%
Other values (64)2089
32.9%
None
ValueCountFrequency (%)
 17
89.5%
é1
 
5.3%
ö1
 
5.3%
Punctuation
ValueCountFrequency (%)
3
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct69
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47281.32407
Minimum1825
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:15:02.357620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1825
5-th percentile9524.15
Q148909.5
median52110
Q352743
95-th percentile57860.6
Maximum61755
Range59930
Interquartile range (IQR)3833.5

Descriptive statistics

Standard deviation13666.57216
Coefficient of variation (CV)0.289048
Kurtosis4.667919626
Mean47281.32407
Median Absolute Deviation (MAD)1802
Skewness-2.323608987
Sum5106383
Variance186775194.7
MonotonicityNot monotonic
2022-05-09T21:15:02.527563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
527438
 
7.4%
519286
 
5.6%
525216
 
5.6%
519944
 
3.7%
521103
 
2.8%
524713
 
2.8%
18253
 
2.8%
570303
 
2.8%
525242
 
1.9%
521042
 
1.9%
Other values (59)68
63.0%
ValueCountFrequency (%)
18253
2.8%
22661
 
0.9%
25041
 
0.9%
64411
 
0.9%
152502
1.9%
173561
 
0.9%
262681
 
0.9%
283461
 
0.9%
306061
 
0.9%
339441
 
0.9%
ValueCountFrequency (%)
617551
 
0.9%
586892
1.9%
584261
 
0.9%
583671
 
0.9%
579531
 
0.9%
576891
 
0.9%
574781
 
0.9%
570303
2.8%
568481
 
0.9%
567831
 
0.9%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
https://www.tvmaze.com/shows/52743/the-penalty-zone
 
8
https://www.tvmaze.com/shows/51928/anitta-made-in-honorio
 
6
https://www.tvmaze.com/shows/52521/rompan-todo-la-historia-del-rock-en-america-latina
 
6
https://www.tvmaze.com/shows/51994/the-ripper
 
4
https://www.tvmaze.com/shows/52110/how-to-ruin-christmas
 
3
Other values (64)
81 

Length

Max length85
Median length61
Mean length53.02777778
Min length41

Characters and Unicode

Total characters5727
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)46.3%

Sample

1st rowhttps://www.tvmaze.com/shows/49630/kontakty
2nd rowhttps://www.tvmaze.com/shows/52316/mertvye-dusi
3rd rowhttps://www.tvmaze.com/shows/52316/mertvye-dusi
4th rowhttps://www.tvmaze.com/shows/48395/wan-sheng-jie
5th rowhttps://www.tvmaze.com/shows/49652/yi-nian-yong-heng

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52743/the-penalty-zone8
 
7.4%
https://www.tvmaze.com/shows/51928/anitta-made-in-honorio6
 
5.6%
https://www.tvmaze.com/shows/52521/rompan-todo-la-historia-del-rock-en-america-latina6
 
5.6%
https://www.tvmaze.com/shows/51994/the-ripper4
 
3.7%
https://www.tvmaze.com/shows/52110/how-to-ruin-christmas3
 
2.8%
https://www.tvmaze.com/shows/52471/the-expanse-aftershow3
 
2.8%
https://www.tvmaze.com/shows/1825/the-expanse3
 
2.8%
https://www.tvmaze.com/shows/57030/gjor-det-sjol3
 
2.8%
https://www.tvmaze.com/shows/52524/forever-love2
 
1.9%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
1.9%
Other values (59)68
63.0%

Length

2022-05-09T21:15:02.704929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52743/the-penalty-zone8
 
7.4%
https://www.tvmaze.com/shows/52521/rompan-todo-la-historia-del-rock-en-america-latina6
 
5.6%
https://www.tvmaze.com/shows/51928/anitta-made-in-honorio6
 
5.6%
https://www.tvmaze.com/shows/51994/the-ripper4
 
3.7%
https://www.tvmaze.com/shows/52110/how-to-ruin-christmas3
 
2.8%
https://www.tvmaze.com/shows/52471/the-expanse-aftershow3
 
2.8%
https://www.tvmaze.com/shows/1825/the-expanse3
 
2.8%
https://www.tvmaze.com/shows/57030/gjor-det-sjol3
 
2.8%
https://www.tvmaze.com/shows/52159/to-love2
 
1.9%
https://www.tvmaze.com/shows/58689/my-supernatural-power2
 
1.9%
Other values (59)68
63.0%

Most occurring characters

ValueCountFrequency (%)
/540
 
9.4%
t466
 
8.1%
w459
 
8.0%
s414
 
7.2%
o359
 
6.3%
e316
 
5.5%
h296
 
5.2%
m280
 
4.9%
a264
 
4.6%
-251
 
4.4%
Other values (29)2082
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4072
71.1%
Other Punctuation864
 
15.1%
Decimal Number540
 
9.4%
Dash Punctuation251
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t466
11.4%
w459
11.3%
s414
10.2%
o359
 
8.8%
e316
 
7.8%
h296
 
7.3%
m280
 
6.9%
a264
 
6.5%
p154
 
3.8%
c152
 
3.7%
Other values (15)912
22.4%
Decimal Number
ValueCountFrequency (%)
5107
19.8%
281
15.0%
465
12.0%
162
11.5%
745
8.3%
040
 
7.4%
639
 
7.2%
335
 
6.5%
835
 
6.5%
931
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/540
62.5%
.216
 
25.0%
:108
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4072
71.1%
Common1655
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t466
11.4%
w459
11.3%
s414
10.2%
o359
 
8.8%
e316
 
7.8%
h296
 
7.3%
m280
 
6.9%
a264
 
6.5%
p154
 
3.8%
c152
 
3.7%
Other values (15)912
22.4%
Common
ValueCountFrequency (%)
/540
32.6%
-251
15.2%
.216
 
13.1%
:108
 
6.5%
5107
 
6.5%
281
 
4.9%
465
 
3.9%
162
 
3.7%
745
 
2.7%
040
 
2.4%
Other values (4)140
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/540
 
9.4%
t466
 
8.1%
w459
 
8.0%
s414
 
7.2%
o359
 
6.3%
e316
 
5.5%
h296
 
5.2%
m280
 
4.9%
a264
 
4.6%
-251
 
4.4%
Other values (29)2082
36.4%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
The Penalty Zone
 
8
Anitta: Made in Honório
 
6
Rompan todo: La historia del rock en América Latina
 
6
The Ripper
 
4
How to Ruin Christmas
 
3
Other values (64)
81 

Length

Max length51
Median length27
Mean length18.31481481
Min length6

Characters and Unicode

Total characters1978
Distinct characters93
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)46.3%

Sample

1st rowКонтакты
2nd rowМёртвые души
3rd rowМёртвые души
4th rowWan Sheng Jie
5th rowYi Nian Yong Heng

Common Values

ValueCountFrequency (%)
The Penalty Zone8
 
7.4%
Anitta: Made in Honório6
 
5.6%
Rompan todo: La historia del rock en América Latina6
 
5.6%
The Ripper4
 
3.7%
How to Ruin Christmas3
 
2.8%
The Expanse Aftershow3
 
2.8%
The Expanse3
 
2.8%
Gjør det sjøl3
 
2.8%
Forever Love2
 
1.9%
Twisted Fate of Love2
 
1.9%
Other values (59)68
63.0%

Length

2022-05-09T21:15:02.923295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the28
 
7.8%
in8
 
2.2%
zone8
 
2.2%
penalty8
 
2.2%
la7
 
1.9%
love7
 
1.9%
honório6
 
1.7%
en6
 
1.7%
expanse6
 
1.7%
anitta6
 
1.7%
Other values (176)269
74.9%

Most occurring characters

ValueCountFrequency (%)
251
 
12.7%
e183
 
9.3%
a124
 
6.3%
o118
 
6.0%
n107
 
5.4%
i103
 
5.2%
r98
 
5.0%
t93
 
4.7%
h65
 
3.3%
s63
 
3.2%
Other values (83)773
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1397
70.6%
Uppercase Letter300
 
15.2%
Space Separator251
 
12.7%
Other Punctuation24
 
1.2%
Decimal Number6
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e183
13.1%
a124
 
8.9%
o118
 
8.4%
n107
 
7.7%
i103
 
7.4%
r98
 
7.0%
t93
 
6.7%
h65
 
4.7%
s63
 
4.5%
l44
 
3.1%
Other values (43)399
28.6%
Uppercase Letter
ValueCountFrequency (%)
T40
13.3%
A24
 
8.0%
M23
 
7.7%
L22
 
7.3%
R20
 
6.7%
S19
 
6.3%
P15
 
5.0%
H15
 
5.0%
C13
 
4.3%
Y12
 
4.0%
Other values (21)97
32.3%
Other Punctuation
ValueCountFrequency (%)
:18
75.0%
'4
 
16.7%
.1
 
4.2%
,1
 
4.2%
Decimal Number
ValueCountFrequency (%)
02
33.3%
22
33.3%
11
16.7%
51
16.7%
Space Separator
ValueCountFrequency (%)
251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1595
80.6%
Common281
 
14.2%
Cyrillic102
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e183
 
11.5%
a124
 
7.8%
o118
 
7.4%
n107
 
6.7%
i103
 
6.5%
r98
 
6.1%
t93
 
5.8%
h65
 
4.1%
s63
 
3.9%
l44
 
2.8%
Other values (42)597
37.4%
Cyrillic
ValueCountFrequency (%)
т9
 
8.8%
р8
 
7.8%
е8
 
7.8%
о7
 
6.9%
а7
 
6.9%
и6
 
5.9%
к6
 
5.9%
н4
 
3.9%
д4
 
3.9%
у4
 
3.9%
Other values (22)39
38.2%
Common
ValueCountFrequency (%)
251
89.3%
:18
 
6.4%
'4
 
1.4%
02
 
0.7%
22
 
0.7%
.1
 
0.4%
,1
 
0.4%
11
 
0.4%
51
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1855
93.8%
Cyrillic102
 
5.2%
None21
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
251
 
13.5%
e183
 
9.9%
a124
 
6.7%
o118
 
6.4%
n107
 
5.8%
i103
 
5.6%
r98
 
5.3%
t93
 
5.0%
h65
 
3.5%
s63
 
3.4%
Other values (48)650
35.0%
Cyrillic
ValueCountFrequency (%)
т9
 
8.8%
р8
 
7.8%
е8
 
7.8%
о7
 
6.9%
а7
 
6.9%
и6
 
5.9%
к6
 
5.9%
н4
 
3.9%
д4
 
3.9%
у4
 
3.9%
Other values (22)39
38.2%
None
ValueCountFrequency (%)
ø9
42.9%
ó6
28.6%
é6
28.6%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size992.0 B
Scripted
47 
Documentary
20 
Talk Show
13 
Reality
10 
Animation
Other values (4)
10 

Length

Max length11
Median length9
Mean length8.537037037
Min length4

Characters and Unicode

Total characters922
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGame Show
2nd rowScripted
3rd rowScripted
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted47
43.5%
Documentary20
18.5%
Talk Show13
 
12.0%
Reality10
 
9.3%
Animation8
 
7.4%
Game Show3
 
2.8%
Sports3
 
2.8%
News2
 
1.9%
Variety2
 
1.9%

Length

2022-05-09T21:15:03.077252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:15:03.231925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted47
37.9%
documentary20
16.1%
show16
 
12.9%
talk13
 
10.5%
reality10
 
8.1%
animation8
 
6.5%
game3
 
2.4%
sports3
 
2.4%
news2
 
1.6%
variety2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
t90
 
9.8%
e84
 
9.1%
i75
 
8.1%
r72
 
7.8%
c67
 
7.3%
S66
 
7.2%
a56
 
6.1%
p50
 
5.4%
d47
 
5.1%
o47
 
5.1%
Other values (17)268
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter782
84.8%
Uppercase Letter124
 
13.4%
Space Separator16
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t90
11.5%
e84
10.7%
i75
9.6%
r72
9.2%
c67
8.6%
a56
 
7.2%
p50
 
6.4%
d47
 
6.0%
o47
 
6.0%
n36
 
4.6%
Other values (8)158
20.2%
Uppercase Letter
ValueCountFrequency (%)
S66
53.2%
D20
 
16.1%
T13
 
10.5%
R10
 
8.1%
A8
 
6.5%
G3
 
2.4%
N2
 
1.6%
V2
 
1.6%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin906
98.3%
Common16
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t90
 
9.9%
e84
 
9.3%
i75
 
8.3%
r72
 
7.9%
c67
 
7.4%
S66
 
7.3%
a56
 
6.2%
p50
 
5.5%
d47
 
5.2%
o47
 
5.2%
Other values (16)252
27.8%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t90
 
9.8%
e84
 
9.1%
i75
 
8.1%
r72
 
7.8%
c67
 
7.3%
S66
 
7.2%
a56
 
6.1%
p50
 
5.4%
d47
 
5.1%
o47
 
5.1%
Other values (17)268
29.1%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size992.0 B
Chinese
32 
English
28 
Norwegian
Russian
Spanish
Other values (11)
26 

Length

Max length10
Median length7
Mean length7.046296296
Min length3

Characters and Unicode

Total characters761
Distinct characters32
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)4.6%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
Chinese32
29.6%
English28
25.9%
Norwegian8
 
7.4%
Russian7
 
6.5%
Spanish7
 
6.5%
Portuguese7
 
6.5%
nan6
 
5.6%
Korean2
 
1.9%
Arabic2
 
1.9%
Japanese2
 
1.9%
Other values (6)7
 
6.5%

Length

2022-05-09T21:15:03.387056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese32
29.6%
english28
25.9%
norwegian8
 
7.4%
russian7
 
6.5%
spanish7
 
6.5%
portuguese7
 
6.5%
nan6
 
5.6%
korean2
 
1.9%
arabic2
 
1.9%
japanese2
 
1.9%
Other values (6)7
 
6.5%

Most occurring characters

ValueCountFrequency (%)
n103
13.5%
e93
12.2%
s90
11.8%
i89
11.7%
h72
9.5%
a44
 
5.8%
g44
 
5.8%
C32
 
4.2%
E28
 
3.7%
l28
 
3.7%
Other values (22)138
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter659
86.6%
Uppercase Letter102
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n103
15.6%
e93
14.1%
s90
13.7%
i89
13.5%
h72
10.9%
a44
6.7%
g44
6.7%
l28
 
4.2%
u23
 
3.5%
r22
 
3.3%
Other values (8)51
7.7%
Uppercase Letter
ValueCountFrequency (%)
C32
31.4%
E28
27.5%
N8
 
7.8%
P7
 
6.9%
S7
 
6.9%
R7
 
6.9%
K3
 
2.9%
A2
 
2.0%
J2
 
2.0%
T2
 
2.0%
Other values (4)4
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin761
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n103
13.5%
e93
12.2%
s90
11.8%
i89
11.7%
h72
9.5%
a44
 
5.8%
g44
 
5.8%
C32
 
4.2%
E28
 
3.7%
l28
 
3.7%
Other values (22)138
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n103
13.5%
e93
12.2%
s90
11.8%
i89
11.7%
h72
9.5%
a44
 
5.8%
g44
 
5.8%
C32
 
4.2%
E28
 
3.7%
l28
 
3.7%
Other values (22)138
18.1%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size992.0 B
[]
27 
['Drama', 'Romance']
11 
['Music']
['Drama', 'Action', 'Crime']
['Comedy']
Other values (26)
46 

Length

Max length42
Median length38
Mean length16.27777778
Min length2

Characters and Unicode

Total characters1758
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)13.9%

Sample

1st row[]
2nd row['Comedy']
3rd row['Comedy']
4th row['Comedy', 'Anime', 'Supernatural']
5th row['Comedy', 'Action', 'Anime', 'Fantasy']

Common Values

ValueCountFrequency (%)
[]27
25.0%
['Drama', 'Romance']11
10.2%
['Music']9
 
8.3%
['Drama', 'Action', 'Crime']8
 
7.4%
['Comedy']7
 
6.5%
['Music', 'History']6
 
5.6%
['Crime']4
 
3.7%
['Science-Fiction', 'Thriller', 'Mystery']3
 
2.8%
['Comedy', 'Children']3
 
2.8%
['Drama', 'Comedy']3
 
2.8%
Other values (21)27
25.0%

Length

2022-05-09T21:15:03.530537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama34
17.6%
27
14.0%
comedy18
9.3%
crime16
8.3%
music15
7.8%
romance14
7.3%
action13
 
6.7%
thriller9
 
4.7%
mystery9
 
4.7%
history9
 
4.7%
Other values (10)29
15.0%

Most occurring characters

ValueCountFrequency (%)
'332
18.9%
[108
 
6.1%
]108
 
6.1%
a103
 
5.9%
r99
 
5.6%
m90
 
5.1%
e89
 
5.1%
i87
 
4.9%
,85
 
4.8%
85
 
4.8%
Other values (23)572
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter866
49.3%
Other Punctuation417
23.7%
Uppercase Letter170
 
9.7%
Open Punctuation108
 
6.1%
Close Punctuation108
 
6.1%
Space Separator85
 
4.8%
Dash Punctuation4
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a103
11.9%
r99
11.4%
m90
10.4%
e89
10.3%
i87
10.0%
o59
6.8%
y54
6.2%
n54
6.2%
c54
6.2%
t45
 
5.2%
Other values (7)132
15.2%
Uppercase Letter
ValueCountFrequency (%)
C39
22.9%
D34
20.0%
M24
14.1%
A19
11.2%
R14
 
8.2%
F13
 
7.6%
T10
 
5.9%
H9
 
5.3%
S7
 
4.1%
W1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
'332
79.6%
,85
 
20.4%
Open Punctuation
ValueCountFrequency (%)
[108
100.0%
Close Punctuation
ValueCountFrequency (%)
]108
100.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1036
58.9%
Common722
41.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a103
 
9.9%
r99
 
9.6%
m90
 
8.7%
e89
 
8.6%
i87
 
8.4%
o59
 
5.7%
y54
 
5.2%
n54
 
5.2%
c54
 
5.2%
t45
 
4.3%
Other values (17)302
29.2%
Common
ValueCountFrequency (%)
'332
46.0%
[108
 
15.0%
]108
 
15.0%
,85
 
11.8%
85
 
11.8%
-4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'332
18.9%
[108
 
6.1%
]108
 
6.1%
a103
 
5.9%
r99
 
5.6%
m90
 
5.1%
e89
 
5.1%
i87
 
4.9%
,85
 
4.8%
85
 
4.8%
Other values (23)572
32.5%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size992.0 B
Running
54 
Ended
48 
To Be Determined

Length

Max length16
Median length11.5
Mean length6.611111111
Min length5

Characters and Unicode

Total characters714
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowEnded
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running54
50.0%
Ended48
44.4%
To Be Determined6
 
5.6%

Length

2022-05-09T21:15:03.659113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:15:03.781879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running54
45.0%
ended48
40.0%
to6
 
5.0%
be6
 
5.0%
determined6
 
5.0%

Most occurring characters

ValueCountFrequency (%)
n216
30.3%
d102
14.3%
e72
 
10.1%
i60
 
8.4%
R54
 
7.6%
u54
 
7.6%
g54
 
7.6%
E48
 
6.7%
12
 
1.7%
T6
 
0.8%
Other values (6)36
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter582
81.5%
Uppercase Letter120
 
16.8%
Space Separator12
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n216
37.1%
d102
17.5%
e72
 
12.4%
i60
 
10.3%
u54
 
9.3%
g54
 
9.3%
o6
 
1.0%
t6
 
1.0%
r6
 
1.0%
m6
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
R54
45.0%
E48
40.0%
T6
 
5.0%
B6
 
5.0%
D6
 
5.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin702
98.3%
Common12
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n216
30.8%
d102
14.5%
e72
 
10.3%
i60
 
8.5%
R54
 
7.7%
u54
 
7.7%
g54
 
7.7%
E48
 
6.8%
T6
 
0.9%
o6
 
0.9%
Other values (5)30
 
4.3%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n216
30.3%
d102
14.3%
e72
 
10.1%
i60
 
8.4%
R54
 
7.6%
u54
 
7.6%
g54
 
7.6%
E48
 
6.7%
12
 
1.7%
T6
 
0.8%
Other values (6)36
 
5.0%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct18
Distinct (%)28.6%
Missing45
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean41.12698413
Minimum2
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:15:03.917175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.2
Q121.5
median45
Q345
95-th percentile117
Maximum120
Range118
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation27.4622862
Coefficient of variation (CV)0.6677437401
Kurtosis2.512863731
Mean41.12698413
Median Absolute Deviation (MAD)15
Skewness1.417039841
Sum2591
Variance754.1771633
MonotonicityNot monotonic
2022-05-09T21:15:04.077447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4525
23.1%
206
 
5.6%
305
 
4.6%
1204
 
3.7%
603
 
2.8%
253
 
2.8%
73
 
2.8%
52
 
1.9%
432
 
1.9%
902
 
1.9%
Other values (8)8
 
7.4%
(Missing)45
41.7%
ValueCountFrequency (%)
21
 
0.9%
41
 
0.9%
52
 
1.9%
73
2.8%
121
 
0.9%
161
 
0.9%
191
 
0.9%
206
5.6%
231
 
0.9%
253
2.8%
ValueCountFrequency (%)
1204
 
3.7%
902
 
1.9%
603
 
2.8%
551
 
0.9%
4525
23.1%
432
 
1.9%
331
 
0.9%
305
 
4.6%
253
 
2.8%
231
 
0.9%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)33.3%
Missing6
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean38.23529412
Minimum2
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:15:04.221473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q120.75
median44
Q349
95-th percentile74.4
Maximum120
Range118
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation23.81911059
Coefficient of variation (CV)0.6229613538
Kurtosis3.091987485
Mean38.23529412
Median Absolute Deviation (MAD)14
Skewness1.259553128
Sum3900
Variance567.3500291
MonotonicityNot monotonic
2022-05-09T21:15:04.365011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4524
22.2%
3010
 
9.3%
509
 
8.3%
205
 
4.6%
255
 
4.6%
55
 
4.6%
494
 
3.7%
73
 
2.8%
123
 
2.8%
603
 
2.8%
Other values (24)31
28.7%
(Missing)6
 
5.6%
ValueCountFrequency (%)
21
 
0.9%
41
 
0.9%
55
4.6%
73
2.8%
81
 
0.9%
91
 
0.9%
101
 
0.9%
111
 
0.9%
123
2.8%
141
 
0.9%
ValueCountFrequency (%)
1203
 
2.8%
1101
 
0.9%
901
 
0.9%
751
 
0.9%
631
 
0.9%
603
 
2.8%
591
 
0.9%
553
 
2.8%
509
8.3%
494
3.7%

_embedded_show_premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
2020-12-16
31 
2020-11-23
 
4
2020-12-14
 
4
2020-12-08
 
3
2020-12-09
 
3
Other values (51)
63 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1080
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)38.9%

Sample

1st row2019-04-03
2nd row2020-12-09
3rd row2020-12-09
4th row2020-04-01
5th row2020-08-12

Common Values

ValueCountFrequency (%)
2020-12-1631
28.7%
2020-11-234
 
3.7%
2020-12-144
 
3.7%
2020-12-083
 
2.8%
2020-12-093
 
2.8%
2020-11-183
 
2.8%
2015-12-143
 
2.8%
2020-07-083
 
2.8%
2013-12-242
 
1.9%
2020-11-242
 
1.9%
Other values (46)50
46.3%

Length

2022-05-09T21:15:04.517271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-1631
28.7%
2020-12-144
 
3.7%
2020-11-234
 
3.7%
2020-12-083
 
2.8%
2020-12-093
 
2.8%
2020-11-183
 
2.8%
2015-12-143
 
2.8%
2020-07-083
 
2.8%
2020-11-042
 
1.9%
2020-12-022
 
1.9%
Other values (46)50
46.3%

Most occurring characters

ValueCountFrequency (%)
2265
24.5%
0247
22.9%
-216
20.0%
1199
18.4%
638
 
3.5%
929
 
2.7%
423
 
2.1%
823
 
2.1%
320
 
1.9%
512
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number864
80.0%
Dash Punctuation216
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2265
30.7%
0247
28.6%
1199
23.0%
638
 
4.4%
929
 
3.4%
423
 
2.7%
823
 
2.7%
320
 
2.3%
512
 
1.4%
78
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1080
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2265
24.5%
0247
22.9%
-216
20.0%
1199
18.4%
638
 
3.5%
929
 
2.7%
423
 
2.1%
823
 
2.1%
320
 
1.9%
512
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2265
24.5%
0247
22.9%
-216
20.0%
1199
18.4%
638
 
3.5%
929
 
2.7%
423
 
2.1%
823
 
2.1%
320
 
1.9%
512
 
1.1%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size992.0 B
nan
60 
2020-12-16
21 
2020-12-30
 
5
2021-01-05
 
4
2022-01-14
 
3
Other values (11)
15 

Length

Max length10
Median length3
Mean length6.111111111
Min length3

Characters and Unicode

Total characters660
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)6.5%

Sample

1st rownan
2nd row2020-12-16
3rd row2020-12-16
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan60
55.6%
2020-12-1621
 
19.4%
2020-12-305
 
4.6%
2021-01-054
 
3.7%
2022-01-143
 
2.8%
2021-01-272
 
1.9%
2020-12-222
 
1.9%
2020-12-232
 
1.9%
2021-01-142
 
1.9%
2021-01-061
 
0.9%
Other values (6)6
 
5.6%

Length

2022-05-09T21:15:04.644237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan60
55.6%
2020-12-1621
 
19.4%
2020-12-305
 
4.6%
2021-01-054
 
3.7%
2022-01-143
 
2.8%
2021-01-272
 
1.9%
2020-12-222
 
1.9%
2020-12-232
 
1.9%
2021-01-142
 
1.9%
2021-01-061
 
0.9%
Other values (6)6
 
5.6%

Most occurring characters

ValueCountFrequency (%)
2145
22.0%
n120
18.2%
0108
16.4%
-96
14.5%
184
12.7%
a60
9.1%
622
 
3.3%
310
 
1.5%
46
 
0.9%
54
 
0.6%
Other values (2)5
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number384
58.2%
Lowercase Letter180
27.3%
Dash Punctuation96
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2145
37.8%
0108
28.1%
184
21.9%
622
 
5.7%
310
 
2.6%
46
 
1.6%
54
 
1.0%
83
 
0.8%
72
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
n120
66.7%
a60
33.3%
Dash Punctuation
ValueCountFrequency (%)
-96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common480
72.7%
Latin180
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2145
30.2%
0108
22.5%
-96
20.0%
184
17.5%
622
 
4.6%
310
 
2.1%
46
 
1.2%
54
 
0.8%
83
 
0.6%
72
 
0.4%
Latin
ValueCountFrequency (%)
n120
66.7%
a60
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2145
22.0%
n120
18.2%
0108
16.4%
-96
14.5%
184
12.7%
a60
9.1%
622
 
3.3%
310
 
1.5%
46
 
0.9%
54
 
0.6%
Other values (2)5
 
0.8%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Memory size992.0 B
nan
10 
https://www.iqiyi.com/a_19rrhllpip.html
https://www.netflix.com/title/81302719
 
6
https://www.netflix.com/title/81006953
 
6
https://www.netflix.com/title/81006684
 
4
Other values (57)
74 

Length

Max length250
Median length83
Mean length45.80555556
Min length3

Characters and Unicode

Total characters4947
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)40.7%

Sample

1st rowhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI
2nd rowhttps://www.ivi.ru/watch/mertvyie-dushi-2020
3rd rowhttps://www.ivi.ru/watch/mertvyie-dushi-2020
4th rowhttps://v.qq.com/detail/a/awnia0n2erqryf3.html
5th rowhttps://v.qq.com/detail/w/ww18u675tfmhas6.html

Common Values

ValueCountFrequency (%)
nan10
 
9.3%
https://www.iqiyi.com/a_19rrhllpip.html8
 
7.4%
https://www.netflix.com/title/813027196
 
5.6%
https://www.netflix.com/title/810069536
 
5.6%
https://www.netflix.com/title/810066844
 
3.7%
https://tv.nrk.no/serie/gjoer-det-sjoel3
 
2.8%
https://www.netflix.com/title/812944173
 
2.8%
https://www.youtube.com/playlist?list=PLWz2DO39R-NU5FW-aFfilRvyeg9oXMTgp3
 
2.8%
https://www.amazon.com/dp/B07YL9WK1S/3
 
2.8%
https://v.youku.com/v_show/id_XNDk4OTUxMzg1Mg==.html?spm=a2hbt.13141534.0.13141534&s=6eefbfbd4befbfbd32ef2
 
1.9%
Other values (52)60
55.6%

Length

2022-05-09T21:15:04.817287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan10
 
9.3%
https://www.iqiyi.com/a_19rrhllpip.html8
 
7.4%
https://www.netflix.com/title/813027196
 
5.6%
https://www.netflix.com/title/810069536
 
5.6%
https://www.netflix.com/title/810066844
 
3.7%
https://tv.nrk.no/serie/gjoer-det-sjoel3
 
2.8%
https://www.netflix.com/title/812944173
 
2.8%
https://www.youtube.com/playlist?list=plwz2do39r-nu5fw-affilrvyeg9oxmtgp3
 
2.8%
https://www.amazon.com/dp/b07yl9wk1s3
 
2.8%
https://so.youku.com/search_video/q_%20%e6%9c%80%e5%88%9d%e7%9a%84%e7%9b%b8%e9%81%87?searchfrom=12
 
1.9%
Other values (52)60
55.6%

Most occurring characters

ValueCountFrequency (%)
/396
 
8.0%
t390
 
7.9%
w218
 
4.4%
.215
 
4.3%
s204
 
4.1%
e201
 
4.1%
o185
 
3.7%
h182
 
3.7%
i177
 
3.6%
l161
 
3.3%
Other values (64)2618
52.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3048
61.6%
Other Punctuation866
 
17.5%
Decimal Number599
 
12.1%
Uppercase Letter331
 
6.7%
Dash Punctuation49
 
1.0%
Math Symbol30
 
0.6%
Connector Punctuation24
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t390
 
12.8%
w218
 
7.2%
s204
 
6.7%
e201
 
6.6%
o185
 
6.1%
h182
 
6.0%
i177
 
5.8%
l161
 
5.3%
p161
 
5.3%
m159
 
5.2%
Other values (16)1010
33.1%
Uppercase Letter
ValueCountFrequency (%)
B50
15.1%
E49
14.8%
A21
 
6.3%
W15
 
4.5%
L13
 
3.9%
F13
 
3.9%
Y13
 
3.9%
D12
 
3.6%
S12
 
3.6%
T12
 
3.6%
Other values (16)121
36.6%
Decimal Number
ValueCountFrequency (%)
0106
17.7%
187
14.5%
885
14.2%
977
12.9%
247
7.8%
444
7.3%
543
7.2%
339
 
6.5%
637
 
6.2%
734
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/396
45.7%
.215
24.8%
%131
 
15.1%
:98
 
11.3%
?15
 
1.7%
&9
 
1.0%
#1
 
0.1%
!1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=28
93.3%
+2
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
-49
100.0%
Connector Punctuation
ValueCountFrequency (%)
_24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3379
68.3%
Common1568
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t390
 
11.5%
w218
 
6.5%
s204
 
6.0%
e201
 
5.9%
o185
 
5.5%
h182
 
5.4%
i177
 
5.2%
l161
 
4.8%
p161
 
4.8%
m159
 
4.7%
Other values (42)1341
39.7%
Common
ValueCountFrequency (%)
/396
25.3%
.215
13.7%
%131
 
8.4%
0106
 
6.8%
:98
 
6.2%
187
 
5.5%
885
 
5.4%
977
 
4.9%
-49
 
3.1%
247
 
3.0%
Other values (12)277
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/396
 
8.0%
t390
 
7.9%
w218
 
4.4%
.215
 
4.3%
s204
 
4.1%
e201
 
4.1%
o185
 
3.7%
h182
 
3.7%
i177
 
3.6%
l161
 
3.3%
Other values (64)2618
52.9%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.30555556
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:15:05.157661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q116.5
median18
Q344.75
95-th percentile83.55
Maximum99
Range98
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation26.07123897
Coefficient of variation (CV)0.8327991154
Kurtosis0.2559407952
Mean31.30555556
Median Absolute Deviation (MAD)9
Skewness1.214491237
Sum3381
Variance679.7095016
MonotonicityNot monotonic
2022-05-09T21:15:05.333774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1824
22.2%
779
 
8.3%
136
 
5.6%
176
 
5.6%
64
 
3.7%
293
 
2.8%
513
 
2.8%
103
 
2.8%
253
 
2.8%
993
 
2.8%
Other values (34)44
40.7%
ValueCountFrequency (%)
11
 
0.9%
22
 
1.9%
31
 
0.9%
51
 
0.9%
64
3.7%
72
 
1.9%
83
2.8%
103
2.8%
111
 
0.9%
136
5.6%
ValueCountFrequency (%)
993
 
2.8%
911
 
0.9%
881
 
0.9%
861
 
0.9%
791
 
0.9%
779
8.3%
721
 
0.9%
691
 
0.9%
641
 
0.9%
551
 
0.9%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size992.0 B
nan
107 
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}
 
1

Length

Max length66
Median length3
Mean length3.583333333
Min length3

Characters and Unicode

Total characters387
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan107
99.1%
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}1
 
0.9%

Length

2022-05-09T21:15:05.498690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:15:05.626722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan107
94.7%
name1
 
0.9%
ukraine1
 
0.9%
code1
 
0.9%
ua1
 
0.9%
timezone1
 
0.9%
europe/zaporozhye1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n217
56.1%
a110
28.4%
'12
 
3.1%
e7
 
1.8%
o5
 
1.3%
5
 
1.3%
:3
 
0.8%
r3
 
0.8%
i2
 
0.5%
p2
 
0.5%
Other values (17)21
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter357
92.2%
Other Punctuation18
 
4.7%
Space Separator5
 
1.3%
Uppercase Letter5
 
1.3%
Open Punctuation1
 
0.3%
Close Punctuation1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n217
60.8%
a110
30.8%
e7
 
2.0%
o5
 
1.4%
r3
 
0.8%
i2
 
0.6%
p2
 
0.6%
z2
 
0.6%
m2
 
0.6%
u1
 
0.3%
Other values (6)6
 
1.7%
Other Punctuation
ValueCountFrequency (%)
'12
66.7%
:3
 
16.7%
,2
 
11.1%
/1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
U2
40.0%
Z1
20.0%
E1
20.0%
A1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
{1
100.0%
Close Punctuation
ValueCountFrequency (%)
}1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin362
93.5%
Common25
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n217
59.9%
a110
30.4%
e7
 
1.9%
o5
 
1.4%
r3
 
0.8%
i2
 
0.6%
p2
 
0.6%
z2
 
0.6%
U2
 
0.6%
m2
 
0.6%
Other values (10)10
 
2.8%
Common
ValueCountFrequency (%)
'12
48.0%
5
20.0%
:3
 
12.0%
,2
 
8.0%
/1
 
4.0%
{1
 
4.0%
}1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n217
56.1%
a110
28.4%
'12
 
3.1%
e7
 
1.8%
o5
 
1.3%
5
 
1.3%
:3
 
0.8%
r3
 
0.8%
i2
 
0.5%
p2
 
0.5%
Other values (17)21
 
5.4%

_embedded_show_summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Memory size992.0 B
nan
11 
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>
<p><b>Anitta: Made in Honório </b>shows, in an unprecedented way, the private life and career of the singer Anitta, icon of funk and pop in Brazil. The show closely follows the controversial and fascinating artist in her intimacy and during the preparation of important shows on different stages around the world. It also shows her on the road and in meetings with her staff, while accumulating two roles: as an artist and entrepreneur.</p>
 
6
<p>Soda Stereo, Café Tacvba, Aterciopelados and others figure in this 50-year history of Latin American rock through dictatorships, disasters and dissent.</p>
 
6
<p>For five years, between 1975 to 1980, the Yorkshire Ripper murders cast a dark shadow over the lives of women in the North of England. 13 women were dead and the police seemed incapable of catching the killer. No one felt safe - and every man was a suspect.</p>
 
4
Other values (54)
73 

Length

Max length913
Median length470
Mean length275.1944444
Min length3

Characters and Unicode

Total characters29721
Distinct characters96
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)36.1%

Sample

1st rownan
2nd row<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>
3rd row<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>
4th row<p>The pure and cute little devil Nini lives in Room 1031 of Wan Sheng Street Apartment. His roommates are no ordinary people: The lazy and wacky vampire Ira who spends most of his time gaming or watching TV, the unlucky dancer werewolf Vladimir, the serious and old-fashioned angel landlord Lynn and his naive little sister Lily, and many more.<br /><br />What none of them realize is that the power of the demon king is sleeping inside of Nini. With evil forces in hot pursuit, can Nini and his friends head off disaster?</p>
5th row<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>

Common Values

ValueCountFrequency (%)
nan11
 
10.2%
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>8
 
7.4%
<p><b>Anitta: Made in Honório </b>shows, in an unprecedented way, the private life and career of the singer Anitta, icon of funk and pop in Brazil. The show closely follows the controversial and fascinating artist in her intimacy and during the preparation of important shows on different stages around the world. It also shows her on the road and in meetings with her staff, while accumulating two roles: as an artist and entrepreneur.</p>6
 
5.6%
<p>Soda Stereo, Café Tacvba, Aterciopelados and others figure in this 50-year history of Latin American rock through dictatorships, disasters and dissent.</p>6
 
5.6%
<p>For five years, between 1975 to 1980, the Yorkshire Ripper murders cast a dark shadow over the lives of women in the North of England. 13 women were dead and the police seemed incapable of catching the killer. No one felt safe - and every man was a suspect.</p>4
 
3.7%
<p>Morten shows you how you can make something cool with what you have at home!</p>3
 
2.8%
<p>Free-spirited Tumi always manages to make a mess of things. Can she make it through this holiday family reunion without ruining it completely?</p>3
 
2.8%
<p>Ty Franck and Wes Chatham dive into the development, behind-the-scenes, and easter eggs of Season 5 of <i>The Expanse.</i></p>3
 
2.8%
<p>A thriller set two hundred years in the future, <b>The Expanse</b> follows the case of a missing young woman who brings a hardened detective and a rogue ship's captain together in a race across the solar system to expose the greatest conspiracy in human history.</p>3
 
2.8%
<p>A story that follows a detective in the major crimes division of Nan Xing City Police Department. Together with a woman who has super memory, he upholds the law one case at a time in solving murders, burglaries and bringing down a narcotics manufacturing facility. Jing Chu is a young and capable detective. Due to his repeated merits from cracking big cases, he is promoted to the position of major crimes division vice-captain at Nan Xing city and starts to work alongside his new team. Because of a murder case, he meets Yang Mian Mian, a young woman who possesses a photographic memory. He soon realizes that Mian Mian seems to have a deep connection to his father's mysterious death many years ago. Meanwhile, a famous blogger and a member of an idol girl group die</p>2
 
1.9%
Other values (49)59
54.6%

Length

2022-05-09T21:15:05.770972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the234
 
4.7%
and202
 
4.1%
a165
 
3.3%
of140
 
2.8%
to126
 
2.5%
in120
 
2.4%
his74
 
1.5%
with43
 
0.9%
he39
 
0.8%
who38
 
0.8%
Other values (1406)3806
76.3%

Most occurring characters

ValueCountFrequency (%)
4871
16.4%
e2642
 
8.9%
a1965
 
6.6%
t1780
 
6.0%
n1732
 
5.8%
o1705
 
5.7%
i1675
 
5.6%
s1520
 
5.1%
r1440
 
4.8%
h1118
 
3.8%
Other values (86)9273
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22330
75.1%
Space Separator4879
 
16.4%
Uppercase Letter922
 
3.1%
Other Punctuation788
 
2.7%
Math Symbol584
 
2.0%
Decimal Number106
 
0.4%
Dash Punctuation73
 
0.2%
Format12
 
< 0.1%
Open Punctuation8
 
< 0.1%
Close Punctuation8
 
< 0.1%
Other values (4)11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2642
11.8%
a1965
 
8.8%
t1780
 
8.0%
n1732
 
7.8%
o1705
 
7.6%
i1675
 
7.5%
s1520
 
6.8%
r1440
 
6.4%
h1118
 
5.0%
l855
 
3.8%
Other values (20)5898
26.4%
Uppercase Letter
ValueCountFrequency (%)
S99
 
10.7%
T88
 
9.5%
A84
 
9.1%
M53
 
5.7%
W49
 
5.3%
L48
 
5.2%
H45
 
4.9%
Y39
 
4.2%
C38
 
4.1%
N36
 
3.9%
Other values (16)343
37.2%
Other Punctuation
ValueCountFrequency (%)
,267
33.9%
.238
30.2%
/157
19.9%
'46
 
5.8%
"33
 
4.2%
:23
 
2.9%
!12
 
1.5%
?11
 
1.4%
1
 
0.1%
Decimal Number
ValueCountFrequency (%)
030
28.3%
125
23.6%
515
14.2%
29
 
8.5%
98
 
7.5%
88
 
7.5%
76
 
5.7%
35
 
4.7%
Other Letter
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Dash Punctuation
ValueCountFrequency (%)
-65
89.0%
7
 
9.6%
1
 
1.4%
Space Separator
ValueCountFrequency (%)
4871
99.8%
 8
 
0.2%
Math Symbol
ValueCountFrequency (%)
>292
50.0%
<292
50.0%
Open Punctuation
ValueCountFrequency (%)
(7
87.5%
[1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
)7
87.5%
]1
 
12.5%
Format
ValueCountFrequency (%)
12
100.0%
Modifier Letter
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23252
78.2%
Common6461
 
21.7%
Katakana4
 
< 0.1%
Han4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2642
 
11.4%
a1965
 
8.5%
t1780
 
7.7%
n1732
 
7.4%
o1705
 
7.3%
i1675
 
7.2%
s1520
 
6.5%
r1440
 
6.2%
h1118
 
4.8%
l855
 
3.7%
Other values (46)6820
29.3%
Common
ValueCountFrequency (%)
4871
75.4%
>292
 
4.5%
<292
 
4.5%
,267
 
4.1%
.238
 
3.7%
/157
 
2.4%
-65
 
1.0%
'46
 
0.7%
"33
 
0.5%
030
 
0.5%
Other values (22)170
 
2.6%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29667
99.8%
None23
 
0.1%
Punctuation21
 
0.1%
Katakana5
 
< 0.1%
CJK4
 
< 0.1%
Dingbats1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4871
16.4%
e2642
 
8.9%
a1965
 
6.6%
t1780
 
6.0%
n1732
 
5.8%
o1705
 
5.7%
i1675
 
5.6%
s1520
 
5.1%
r1440
 
4.9%
h1118
 
3.8%
Other values (67)9219
31.1%
Punctuation
ValueCountFrequency (%)
12
57.1%
7
33.3%
1
 
4.8%
1
 
4.8%
None
ValueCountFrequency (%)
 8
34.8%
é7
30.4%
ó6
26.1%
ā1
 
4.3%
å1
 
4.3%
Katakana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1629093515
Minimum1606418164
Maximum1652080636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size992.0 B
2022-05-09T21:15:05.950764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1606418164
5-th percentile1608671915
Q11612297661
median1629863948
Q31647581886
95-th percentile1651685992
Maximum1652080636
Range45662472
Interquartile range (IQR)35284225

Descriptive statistics

Standard deviation17280939.27
Coefficient of variation (CV)0.01060770245
Kurtosis-1.746788849
Mean1629093515
Median Absolute Deviation (MAD)17566287
Skewness0.06201301588
Sum1.759420996 × 1011
Variance2.986308622 × 1014
MonotonicityNot monotonic
2022-05-09T21:15:06.133193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16097998968
 
7.4%
16086719156
 
5.6%
16122976616
 
5.6%
16422453084
 
3.7%
16394004903
 
2.8%
16124799203
 
2.8%
16501625523
 
2.8%
16341681393
 
2.8%
16124781452
 
1.9%
16095351412
 
1.9%
Other values (59)68
63.0%
ValueCountFrequency (%)
16064181642
 
1.9%
16082530132
 
1.9%
16086719156
5.6%
16090607262
 
1.9%
16095351412
 
1.9%
16096897361
 
0.9%
16097998968
7.4%
16108903401
 
0.9%
16114368421
 
0.9%
16120078311
 
0.9%
ValueCountFrequency (%)
16520806361
0.9%
16519339621
0.9%
16519332091
0.9%
16518386471
0.9%
16517773161
0.9%
16516880411
0.9%
16516821881
0.9%
16516456841
0.9%
16515070911
0.9%
16514170271
0.9%

_links_self_href
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size992.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2189555
 
1
https://api.tvmaze.com/episodes/1955318
 
1
https://api.tvmaze.com/episodes/1996786
 
1
https://api.tvmaze.com/episodes/1949336
 
1
Other values (103)
103 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters4212
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.9%
https://api.tvmaze.com/episodes/21895551
 
0.9%
https://api.tvmaze.com/episodes/19553181
 
0.9%
https://api.tvmaze.com/episodes/19967861
 
0.9%
https://api.tvmaze.com/episodes/19493361
 
0.9%
https://api.tvmaze.com/episodes/19493351
 
0.9%
https://api.tvmaze.com/episodes/19493341
 
0.9%
https://api.tvmaze.com/episodes/19493331
 
0.9%
https://api.tvmaze.com/episodes/19493321
 
0.9%
https://api.tvmaze.com/episodes/19493311
 
0.9%
Other values (98)98
90.7%

Length

2022-05-09T21:15:06.290065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.9%
https://api.tvmaze.com/episodes/23244331
 
0.9%
https://api.tvmaze.com/episodes/19640001
 
0.9%
https://api.tvmaze.com/episodes/19954051
 
0.9%
https://api.tvmaze.com/episodes/20077601
 
0.9%
https://api.tvmaze.com/episodes/19857891
 
0.9%
https://api.tvmaze.com/episodes/20396221
 
0.9%
https://api.tvmaze.com/episodes/20396231
 
0.9%
https://api.tvmaze.com/episodes/23244271
 
0.9%
https://api.tvmaze.com/episodes/23244281
 
0.9%
Other values (98)98
90.7%

Most occurring characters

ValueCountFrequency (%)
/432
 
10.3%
p324
 
7.7%
s324
 
7.7%
e324
 
7.7%
t324
 
7.7%
o216
 
5.1%
a216
 
5.1%
i216
 
5.1%
.216
 
5.1%
m216
 
5.1%
Other values (16)1404
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2700
64.1%
Other Punctuation756
 
17.9%
Decimal Number756
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p324
12.0%
s324
12.0%
e324
12.0%
t324
12.0%
o216
8.0%
a216
8.0%
i216
8.0%
m216
8.0%
h108
 
4.0%
d108
 
4.0%
Other values (3)324
12.0%
Decimal Number
ValueCountFrequency (%)
9119
15.7%
2113
14.9%
199
13.1%
096
12.7%
365
8.6%
663
8.3%
859
7.8%
751
6.7%
449
6.5%
542
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/432
57.1%
.216
28.6%
:108
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2700
64.1%
Common1512
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/432
28.6%
.216
14.3%
9119
 
7.9%
2113
 
7.5%
:108
 
7.1%
199
 
6.5%
096
 
6.3%
365
 
4.3%
663
 
4.2%
859
 
3.9%
Other values (3)142
 
9.4%
Latin
ValueCountFrequency (%)
p324
12.0%
s324
12.0%
e324
12.0%
t324
12.0%
o216
8.0%
a216
8.0%
i216
8.0%
m216
8.0%
h108
 
4.0%
d108
 
4.0%
Other values (3)324
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/432
 
10.3%
p324
 
7.7%
s324
 
7.7%
e324
 
7.7%
t324
 
7.7%
o216
 
5.1%
a216
 
5.1%
i216
 
5.1%
.216
 
5.1%
m216
 
5.1%
Other values (16)1404
33.3%

Interactions

2022-05-09T21:14:52.610098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:17.882613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:26.564038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:29.865202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:33.638439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:36.884398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:42.941758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:45.214151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:48.644207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:54.490463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:20.258293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:27.929941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:31.278449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:34.997550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:38.543612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:43.799720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:46.563308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:50.257781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:54.650337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:20.953826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:28.088943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:31.426992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:35.149143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:39.057914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:43.946204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:46.741123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:50.448656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:54.833847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:21.625632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:28.241897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:31.581269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:35.293152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:39.697718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:44.069227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:46.909230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:50.619842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:54.996605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:22.240503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:28.415070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:31.743876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:35.468208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:40.142531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:44.200851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:47.151476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:50.863738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:55.918560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:23.576102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:29.271528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:32.730635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:36.207628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:41.411486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:44.489043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:48.002371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:52.011009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:56.059699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:23.977095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:29.444520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:33.073052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:36.355368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:41.672366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:44.802345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:48.160626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:52.180376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:56.194615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:24.728203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:29.591198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:33.265013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:36.530997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:42.065493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:44.948028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:48.331753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:52.332077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:56.309529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:25.672287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:29.732903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:33.471623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:36.715968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:42.512866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:45.080802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:48.499131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:14:52.464894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:15:06.411467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:15:06.580080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:15:06.806460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:15:07.020861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:15:07.356052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:14:56.564713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:14:57.614290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:14:57.909237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:14:58.236877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
02179613https://www.tvmaze.com/episodes/2179613/kontakty-1x30-kontakty-v-telefone-ili-makarova-ruslan-belyj-guram-amaran-andrej-beburisvili-sasa-vasКОНТАКТЫ в телефоне Ильи Макарова: Руслан Белый, Гурам Амарян, Андрей Бебуришвили, Саша Ваш1.030.0regular2020-12-1612:002020-12-16T00:00:00+00:0032.0nan49630https://www.tvmaze.com/shows/49630/kontaktyКонтактыGame ShowRussian[]Running30.041.02019-04-03nanhttps://www.youtube.com/playlist?list=PLZ1FUdedsrSJubWkFFh5vKHzawzQk1mYI53.0nannan1.651688e+09https://api.tvmaze.com/episodes/1977902
11983259https://www.tvmaze.com/episodes/1983259/mertvye-dusi-1x03-seria-3Серия 31.03.0regular2020-12-16nan2020-12-16T00:00:00+00:0043.0nan52316https://www.tvmaze.com/shows/52316/mertvye-dusiМёртвые душиScriptedRussian['Comedy']Ended43.043.02020-12-092020-12-16https://www.ivi.ru/watch/mertvyie-dushi-202018.0nan<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>1.608253e+09https://api.tvmaze.com/episodes/2015818
21983260https://www.tvmaze.com/episodes/1983260/mertvye-dusi-1x04-seria-4Серия 41.04.0regular2020-12-16nan2020-12-16T00:00:00+00:0043.0nan52316https://www.tvmaze.com/shows/52316/mertvye-dusiМёртвые душиScriptedRussian['Comedy']Ended43.043.02020-12-092020-12-16https://www.ivi.ru/watch/mertvyie-dushi-202018.0nan<p>A modern adaptation of the classic novel-poem by Nikolai Gogol. The action takes place in our days. Chichikov, an enterprising and not without charm official from Moscow, this time does not buy up dead souls from small-minded provincials, but sells them places in the cemetery next to celebrities. He arrives in the County town of N and quickly makes" big and small " connections there. These bright acquaintances will benefit him and open up new opportunities for personal gain.</p>1.608253e+09https://api.tvmaze.com/episodes/1964000
31997413https://www.tvmaze.com/episodes/1997413/wan-sheng-jie-2x12-ah-goodbye-friendsAh, Goodbye Friends2.012.0regular2020-12-1610:002020-12-16T02:00:00+00:004.0nan48395https://www.tvmaze.com/shows/48395/wan-sheng-jieWan Sheng JieAnimationChinese['Comedy', 'Anime', 'Supernatural']Running4.04.02020-04-01nanhttps://v.qq.com/detail/a/awnia0n2erqryf3.html17.0nan<p>The pure and cute little devil Nini lives in Room 1031 of Wan Sheng Street Apartment. His roommates are no ordinary people: The lazy and wacky vampire Ira who spends most of his time gaming or watching TV, the unlucky dancer werewolf Vladimir, the serious and old-fashioned angel landlord Lynn and his naive little sister Lily, and many more.<br /><br />What none of them realize is that the power of the demon king is sleeping inside of Nini. With evil forces in hot pursuit, can Nini and his friends head off disaster?</p>1.647194e+09https://api.tvmaze.com/episodes/1995405
42095628https://www.tvmaze.com/episodes/2095628/yi-nian-yong-heng-1x21-episode-21Episode 211.021.0regular2020-12-1610:002020-12-16T02:00:00+00:0019.0nan49652https://www.tvmaze.com/shows/49652/yi-nian-yong-hengYi Nian Yong HengAnimationChinese['Comedy', 'Action', 'Anime', 'Fantasy']Running19.019.02020-08-12nanhttps://v.qq.com/detail/w/ww18u675tfmhas6.html17.0nan<p>One will to create oceans. One will to summon the mulberry fields.<br /><br />One will to slaughter countless devils. One will to eradicate innumerable immortals.<br /><br />Only my will… is eternal.<br /><br />A Will Eternal tells the tale of Bai Xiaochun, an endearing but exasperating young man who is driven primarily by his fear of death and desire to live forever, but who deeply values friendship and family.<br /><br />(Source: Novel Updates)</p>1.649494e+09https://api.tvmaze.com/episodes/2007760
52096298https://www.tvmaze.com/episodes/2096298/no-turning-back-romance-1x04-441.04.0regular2020-12-16nan2020-12-16T03:00:00+00:0012.0nan55002https://www.tvmaze.com/shows/55002/no-turning-back-romanceNo Turning Back RomanceScriptedKorean[]EndedNaN12.02020-12-082021-01-06nan20.0nan<p>A teen romance of So Dam, a sixteen-year-old girl, who has never dated before but she receives her first-ever love confession from a mysterious boy. She is looking for the boy who secretly confessed to her while she was asleep on her desk. The clues include a male voice, mango fruit scent and gym uniform. She must piece the puzzle to find that person among the likely candidates that include hot shots Park Ji Hoo, Jeong Han Kyul, and Joo In Hyuk.</p>1.621617e+09https://api.tvmaze.com/episodes/1985789
62030020https://www.tvmaze.com/episodes/2030020/dolls-frontline-2x12-episode-12Episode 122.012.0regular2020-12-1612:002020-12-16T04:00:00+00:005.0nan45713https://www.tvmaze.com/shows/45713/dolls-frontlineDolls' FrontlineAnimationChinese['Comedy', 'Anime', 'Science-Fiction']Ended5.05.02019-07-282020-12-16https://www.bilibili.com/bangumi/media/md2822989520.0nan<p>Re-imagines famous firearms as moe girls with machine bodies that are known as T-Dolls.</p>1.613087e+09https://api.tvmaze.com/episodes/2039622
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Last rows

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1032192624https://www.tvmaze.com/episodes/2192624/diti-proti-zirok-2x11-vypusk-11-ekaterina-kuhar-evgenij-klopotenko-ana-zaecВыпуск 11 (Екатерина Кухар, Евгений Клопотенко, Яна Заец)2.011.0regular2020-12-1619:002020-12-16T17:00:00+00:0090.0nan44675https://www.tvmaze.com/shows/44675/diti-proti-zirokДіти проти зірокGame ShowUkrainian['Action', 'Family']Running90.090.02019-09-25nanhttps://novy.tv/ua/deti-protiv-zvezd/21.0{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}nan1.640953e+09https://api.tvmaze.com/episodes/2001674
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1071945146https://www.tvmaze.com/episodes/1945146/noblesse-1x11-lord-lost-childLord / Lost Child1.011.0regular2020-12-1600:002020-12-17T05:00:00+00:0025.0<p>After being summoned by Lord Raskreia, Seira headed to her hometown of Lukedonia. Raskreia finds out about the false reports to protect Raizel and decides to punish Seira and Gejutel. Meanwhile, Raizel, Frankenstein, and Regis arrive in Lukedonia to save Seira and Gejutel.</p>49732https://www.tvmaze.com/shows/49732/noblesseNoblesseAnimationJapanese['Anime', 'Supernatural']Ended25.025.02015-12-042020-12-30https://noblesse-anime.com/44.0nan<p>Raizel awakens from his 820-year slumber. He holds the special title of Noblesse, a pure-blooded Noble and protector of all other Nobles. In an attempt to protect Raizel, his servant Frankenstein enrolls him at Ye Ran High School, where Raizel learns the simple and quotidian routines of the human world through his classmates. However, the Union, a secret society plotting to take over the world, dispatches modified humans and gradually encroaches on Raizel's life, causing him to wield his mighty power to protect those around him... After 820 years of intrigue, the secrets behind his slumber are finally revealed, and Raizel's absolute protection as the Noblesse begins!</p>1.648717e+09https://api.tvmaze.com/episodes/2034364